Melanoma is a type of skin cancer that is one of the most aggressive, and its diagnosis requires quick and accurate identification to increase patient survival. This paper draws a comparative analysis of advanced deep learning systems, namely, VGG and traditional CNN systems, used in automatic melanoma detection in dermoscopic images. The techniques included preprocessing of dermoscopic sample datasets, training of both VGG and baseline CNN, and the testing of their classification by confusion matrices and receiver operating characteristic (ROC) analysis. The results showed that VGG model was more inclined to have high true positive rate of melanoma, and had 20/20 correct melanoma and 0.92 area under ROC curve (AUC) in its confusion chart. The CNN model did a little better, with a slightly higher AUC of 0.95 and lower false negative percentages at normal. These results show that deep learning models can be used to advance clinical and face-to-face diagnosis of melanoma; both models provide strong performance.
A. S.· International Conference on...· 0 citations
Proper diagnosis and prognosis of colon cancer is essential to enhancing patient outcomes and informing individual treatment plans. This paper has formulated a deep learning model that is powered by the R-MobileNet design to perform effective colon cancer classification through clinical structured data. The model relied on a variety of data, featuring systematic pre-processing measures and careful validation to achieve as much predictive reliability as possible. Measures of evaluation related to ROC curve analysis, classification accuracy, predicted class distribution, and confusion matrix were evaluated, and the R-MobileNet model consistently performed well on validation datasets, with an area under the curve (AUC) of 0.75 and a high classification accuracy of 0.96. Predicted classes distribution and confusion matrix indicate successful discrimination of the relevant cell types and highlight the ability of this method to accurately detect and stratify prognosis. These findings confirm the incorporation of advanced deep learning algorithms such as R-MobileNet into the clinical decision-making process in managing colon cancer.
A. S.· International Conference on...· 0 citations
Epilepsy is a long-lasting neurological condition that is characterized by repeated seizures and affects millions of people across the world. Traditional methods of diagnosis that are based on manual interpretation of the clinical examinations and electroencephalogram (EEG) are frequently time-consuming and subjective. Thus, early and correct diagnosis is paramount to successful treatment and better patient outcomes. To improve the efficiency and accuracy of diagnoses, the current study proposes an automated system of epilepsy diagnosis using the ResNet152 deep learning architecture. This was trained and tested on a labelled dataset comprising of epileptic and non-epileptic cases. To enhance the performance of the models, preprocessing methods such as normalization and feature extraction were employed. A classification method based on risk score was used and the model was tested using metrics like accuracy, recall, precision, F1-score, confusion matrix and precision-recall curves with threshold optimization. The findings reveal that there is stable validation accuracy of about 8384, which means that there is a consistent learning behaviour. The confusion matrix indicates that it has a high specificity of 586 correctly identified true negatives but low sensitivity with only 2 true positives and 112 false negatives. Moreover, the precision-recall curve (AUPRC = 0.19) indicates the trade-off between precision and recall as a sign of class imbalance issues. In general, the suggested ResNet152-based framework has a high potential to be used as an automated screening tool, especially to detect non-epileptic cases. Its sensitivity, class balancing and optimization techniques, however, need to be improved to elevate its dependability in the clinical application of detecting early epilepsy.
A. S.· 2026 4th International Confe...· 0 citations
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